Substrates Formulated with Biochar for Seedling Production of Moringa oleifera Lam.
Bibliographic record
Abstract
Moringa oleifera is an allogamous plant that is propagated by seeds and cuttings, rich in vitamin A and C, phosphorus, calcium and proteins. However, its best propagation form for the large-scale production is still unknown, as well the most suitable substrate for seedling production. Production of forest species seedlings with a high quality is directly related to physical and chemical substrates properties. Using biochar as substrate for seedlings production is an economical and sustainable solution for this proposal. The aim of this study was to evaluate the influence of three biochar types, in two concentrations, for production of Moringa oleifera seedlings. Three types of substrates were formulated using residues of dry coconut shells, sewage sludge and orange bagasse. The experiment was carried out in a greenhouse, installed in a completely randomized design, consisting of seven treatments (substrates formulated with biochar in two doses, 1 and 2%) and the control, with four replicates, totalizing 28 seedlings. One month after sowing, the seedlings were evaluated biweekly for 60 days in relation to stem base diameter, shoot heigh, leaves number, shoot dry matter, root dry matter, total seedling dry matter and Dickson Quality Index. The substrate formulated using sewage sludge biochar at 2% provided a greater increasing in shoot dry matter. This concentration improved seedling height and stem base diameter in 10.5 and 0.83 mm, respectively, compared to the control. In general, biochar improved physical and chemical soil quality, promoting a better M. oleifera seedlings development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".